It is becoming increasingly important in pharmacology, anatomical pathology and biopharmaceutical research to analyze human tissue samples that are stained with multiple biomarkers. How the same tissue sample reacts to staining by different biomarkers can be determined by slicing the tissue into multiple very thin slices in the z dimension and then separately staining the slices. The correlated analysis of different biomarker staining provides a higher quality medical evaluation than separately analyzing how the same tissue reacts to different biomarkers.
In order to determine how different biomarkers stain the same tissue structures, however, digital images of the slices must be co-registered to indicate which tissue structures in one slice correspond to the tissue structures in the other slices. Co-registration of the digital images is possible only if the thickness of the slices is very thin such that cross sections of the same structures will appear in the digital images of multiple slices. For example, multiple slices may pass through the membrane of a single cell, and it may be possible to determine that the various membrane outlines correspond to the same cell even though the membrane outlines are not identical. Co-registration of the digital images involves mapping of pixels from the digital image of one slice to the related pixels of the digital image of the adjacent slice. Spatial translation and rotation transforms are defined that maximize cross-correlation between corresponding structures in the two images by mapping the pixels from one image to the corresponding pixels of the other image.
Determining corresponding tissue structures to use for co-registration, however, is computationally intensive because digital images of tissue slices typically have a very high spectral resolution, which can be on the order of several Giga-pixels. Performing segmentation on all of the structures in images of adjacent slices and then comparing each structure in one image to all of the structures in the other image to find corresponding structures would not be computationally feasible. Thus, segmentation is typically performed on low-resolution superimages of the tissue slices in order to find structures to use for co-registration. But co-registration performed using low-resolution structures is consequently imprecise. A precise method of co-registration is sought that does not require the segmentation of entire high-resolution images of adjacent tissue slices.
An object-based analysis of the stained structures in each image is performed that allows the results of the different staining to be visually enhanced for better correlation. Once the images of differently stained tissue slices are segmented, enhanced and co-registered, the physician or researcher views the different results on the same structures to make a medical evaluation. A method is sought for displaying the various different staining results to the physician or researcher that simultaneously depicts corresponding structures in the various digital images of differently stained tissue.